The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Video compression coding process using motion compensation is based on the assumption of ‘temporal similarity’ meaning the pixel values belonging to an image in a video sequence are very similar to the pixel values of the previous image. However, because the total transmission/recording of such correspondences of the respective pixels to the positions of the previous image pixels involves too much amount of information to transmit/record to negate taking the full advantage by the temporal similarity, the most currently existing video compression methods are merely providing information on limitedly selected temporal similarity. A representative example is the block based motion compensation method wherein the image to be encoded is divided into nonoverlapping blocks in certain block units and a single motion per block is transmitted to apply a same positional correspondence to all of the pixels in the respective blocks in a decoding stage. Although such block based motion compensation method significantly contributes to compressive encoding by taking advantage of the natural image characterized that the general positional correspondences (motion vector) by the unit of pixel are very smooth, distortions occur in blocks with changes of motions existing in the pixels at the positional correspondences by specific pixels causing the blocking artifact to generate unnaturally distorted reconstruction of pixel values at boundaries of adjacent blocks.
In order to resolve such blocking artifacts and perform an effective compression coding that can insure a higher video quality, various techniques have been developed including a loop-filter method, an overlapped block motion compensation, a variable-size block motion compensation, and others. This overlapped block motion compensation is an effective technique used in performing the motion reconstruction on the respective blocks to substantially reduce the motion reconstruction errors at block boundaries by performing the motion reconstruction through a weighted sum of reconstructed pixels at the present location by using the motions of neighboring blocks and reconstructed pixels of the current block. In such overlapped block motion compensation, determining what weights to use in the weighted sum of the respective reconstructed pixels is the most decisive factor that gives the direct affects on the performance of the motion reconstruction because the very determination of depth of reflecting the motions of neighboring blocks on the motion reconstruction of the current block has the most intimate control over nonsimilarity at the boundaries between the neighboring blocks and the current block.
One of conventional techniques to optimize the weight to the overlapped block motion reconstruction, Michael T. Orchard and Gary J. Sullivan (“Overlapped block motion compensation: an estimation-theoretic approach”, IEEE Trans. on Image Processing, vol. 3, no. 5, pp. 693-699, Wept. 1994) assumes that a pixel value of a current frame equals to an arbitrary pixel value of the prior frame and interprets the pixel value in the current frame as a mean of the conditional probability of the pixel value at the arbitrary position of the prior frame and its positional transition. Through such interpretation, a statistical solution was presented toward the weight value and overlapped block motion estimation method to minimize the energy of the mean-squared error of the overlapped block motion estimation. In addition, Jiro Katto and Mutsumi Ohta(“An analytical framework for overlapped block motion compensation” in Proc. ICASSP'95, 9-12 May, 1995, vol. 4, pp. 2189-2192) and Jiro Katto(“Overlapped motion compensation using a window function which varies in response to an input picture”, U.S. Pat. No. 5,602,593, Feb. 11, 1997), under the assumption that a pixel value in an image has been moved from a particular position in the prior image and that the spatial correlation of pixel values is attenuated exponentially depending on the distance between the pixels, prepared an analytical foundation for the overlapped block motion reconstruction, and thereby dealt with the weight optimization problem of the overlapped block motion reconstruction and suggested the analytical solution thereof. Though such prior arts have their respective theoretical frames in which optimal overlapped block motion weight value have been provided achieving great performance improvements, they entail drawbacks of the inability to directly measure necessary parameters for calculating the weight value or the necessity of large data for the statistical estimations and a high computation complexity resulting in a possible limitation of its real time application.
To solve this problem, Wentao Zheng, Yoshiaki Shishikui, Masahide Naemura, Yasuaki Kanatsugu and Susumu Itoh(“Analysis of overlapped block motion compensation based on a statistical motion distribution model”, in Proc. ICIP'01, 7-10 October 2001, vol. 3, pp. 522-525) and Wentao Zheng, Yoshiaki Shishikui, Masahide Naemura, Yasuaki Kanatsugu and Susumu Itoh(“Analysis of space-dependent characteristics of motion-compensated frame differences based on a statistical motion distribution model”, IEEE Trans. on Image Processing, vol. 11, no. 4, April 2002) assumes the respective pixel motions in a video signal to be statistical models and interprets the characteristics of error residual signal by the pixels locations in the block motion reconstruction and the overlapped block motion reconstruction. In addition, Bo Tao and Michael T. Orchard(“Window design for overlapped block motion compensation through statistical motion modeling” in Proc. Asilomar Conference on Signal, Systems & Computers, 2-5 November 1998, vol. 1, pp. 372-376) and Bo Tao and Michael T. Orchard(“A parametric solution for optimal overlapped block motion compensation”, IEEE Trans. on Image Processing, vol. 10, no. 3, pp. 341-350, March 2001) regarded the uncertainty of an estimated motion vector of a pixel to be the quantization error on the basis of the autocorrelation of pixel brightness values and the statistical model of the motion field and thereby suggested a parameter-based optimal overlapped block motion compensation weight solution. Since these techniques are devised to calculate the optimal overlapped block motion compensation weight based on the estimation of a small number of parameters for expressing the assumed model so as not to require a large amount of image data for the estimation, they may provide the optimal weight matrix while intelligently adapting to changes in probabilistic characteristics of the video sequence. However, they still have unavoidable shortcomings of the over-smoothing problems at the edge regions due to inappropriateness of the models, a performance deterioration problem depending on the measures used in parameter estimation and the estimation accuracy, and other similar problems.